Toward Understanding Information Models of Fault Localization: Elaborate is Not Always Better

Lei, Yan; Mao, Xiaoguang; Zhang, Min; Ren, Jun; Jiang, Yinhua

doi:10.1109/compsac.2017.246

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…It solves the problem of vocabulary differences by separating languages. However, this kind of research ignores the semantic expression differences between natural language and code language, only by considering more bug products to expand the code feature quantity, which makes it difficult to distinguish fine-grained code in the process of word embedding, resulting in this kind of method difficult to apply in fine-grained location, and the usability of the results is questioned [14][15][16][17][18] .…”

Section: Bug Location Based On Irmentioning

confidence: 99%

Bug localization with semantic guidance using pseudo-Siamese network

Zhao

Liu

et al. 2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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Information retrieval (IR) based bug location technology is a relatively recognized lightweight bug location method at present. Most IR bug location methods solve the problem of semantic difference between natural language and code language in the bug report based on code semantic intelligibility, and use semantic similarity to construct IR model to locate source code errors through bug report. However, most IR localization studies take error report description as the guidance for code semantic generation, ignoring the difference between error report and error semantics. Due to the irregular submission of error reports and the ambiguity of error descriptions, this kind of research faces the problem of low location accuracy. We found that the code data is the data written in the specification and verified by the program compilation. Compared with the bug data submitted by the tester, the semantic ambiguity is relatively weaker. Therefore, we use code data as the semantic generation of teacher network training bug data to form SGBL method. In addition, based on the bug data set composed by Jena and other projects, we evaluated the effectiveness of our method and explained the relationship between the semantic extraction method and the bug location accuracy. The experimental results show the effectiveness of the proposed method.

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Section: Bug Location Based On Irmentioning

confidence: 99%

Bug localization with semantic guidance using pseudo-Siamese network

Zhao

Liu

et al. 2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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“…However, the current approaches use this feature of each statement in just one test case, and do not consider their features from the view of all test cases. Consequently, it may cause some bias, posing a negative effect on fault localization effectiveness [7]. Therefore, this paper explores more about deep learning in improving fault localization, i.e., we aim at obtaining more insights by proposing an approach to identify the impact of each statement in all test cases by using the features from the view of all test cases, rather than a binary status, and evaluating our results with large-scale programs.…”

Section: Introductionmentioning

confidence: 99%

TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning

Zhang

Lei

et al. 2019

IEICE Trans. Inf. & Syst.

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Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.

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